Distributed Generalized Nash Equilibrium Seeking for Energy Sharing Games in Prosumers

With the proliferation of distributed generators and energy storage systems, traditional passive consumers in power systems have been gradually evolving into the so-called "prosumers," i.e., proactive consumers, which can both produce and consume power. To encourage energy exchange among p...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 36; no. 5; pp. 3973 - 3986
Main Authors: Wang, Zhaojian, Liu, Feng, Ma, Zhiyuan, Chen, Yue, Jia, Mengshuo, Wei, Wei, Wu, Qiuwei
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Consumers
distributed algorithm
Distributed algorithms
Distributed generation
Energy consumption
Energy sharing
Energy storage
Equivalence
Game theory
Games
generalized Nash Equilibrium
generalized Nash game
Linear programming
Nash equilibrium
Optimization
Power consumption
prosumer
Storage systems
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary:With the proliferation of distributed generators and energy storage systems, traditional passive consumers in power systems have been gradually evolving into the so-called "prosumers," i.e., proactive consumers, which can both produce and consume power. To encourage energy exchange among prosumers, energy sharing is increasingly adopted, which is usually formulated as a generalized Nash game (GNG). In this paper, a distributed approach is proposed to seek the Generalized Nash equilibrium (GNE) of the energy sharing game. To this end, we first prove the strong monotonicity of the game. Then, the GNG is converted into an equivalent optimization problem. An algorithm based on Nesterov's methods is thereby devised to solve the equivalent problem and consequently find the GNE in a distributed manner. The convergence of the proposed algorithm is proved rigorously based on the nonexpansive operator theory. The performance of the algorithm is validated by experiments with three prosumers, and the scalability is tested by simulations using 1888 prosumers.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3058675